The task of aspect sentiment triplet extraction (ASTE) mainly detects aspect terms and their corresponding opinion terms and sentiment polarities from sentences. However, when extracting multi-word aspect terms and opinion terms, it is impossible to accurately extract all words. The existence of repeated aspect terms and opinion terms makes it difficult for previous studies to capture all the correlations between aspect terms and opinion terms in word pairs. In response to these problems, we propose a framework based on syntactic enhanced multitasking learning to perform the task of end-to-end sentiment triplet extraction. The syntactic structure of a sentence reflects syntactic attributes and dependency or association information, therefore having a positive effect on the extraction task and the sentiment classification task. The proposed model utilizes dependency syntactic embedding graph convolutional network to fully mine syntactic features in sentences, and then transmits these features to 3 sub-tasks including aspect terms extraction, opinion terms extraction and sentiment analysis, thus realizing the effective fusion of syntactic information and multi-task joint learning framework. The model is evaluated in the sentiment analysis task upon 4 English datasets and 1 Chinese dataset. The experimental results show that the proposed model is effective and significantly better than other baseline models. At the same time, the results of specific case analysis prove that the method solves the problem of multiple words and repeated words to a certain extent.